Why ROT Data Must be Effectively Managed: Definition and Best Practices

Data management is critical for nearly every business, but properly handling the data you need is only part of the puzzle — you also need to manage data that’s no longer useful.

Redundant, Obsolete, and Trivial (ROT) data represents any data that is no longer useful to the company or relevant in a meaningful way. However, ROT data is typically still stored, backed up, and even incorrectly used, each of which can negatively impact the business.

ROT data management is a series of processes and platforms that identify and remove data that’s become useless to the organization. These data management practices can help cut costs, improve productivity, and bolster security.

It’s estimated that between 60-80% of an organization’s data is ROT data — managing this data is critical to mitigating risks and management costs. Storing and using data of no value to the organization can inflate IT costs well beyond what’s necessary.

Is ROT data costing your business or affecting critical business processes? And how can you discover and destroy this data? Keep reading as we explore why ROT data management is critical and the best practices you should be following to build your new program.

What is ROT Data?

Redundant, Obsolete, and Trivial (ROT) data describes any data that is no longer relevant and necessary or never was to begin with. Let’s define each of these three categories of unnecessary data:

• Redundant data has duplicates stored in multiple systems and is common in intranet systems.
• Trivial data doesn’t need to be stored as it provides no value to the business, which means it can be removed without any effects.
• Obsolete data is no longer accurate or in use, typically because the information has changed without an update to the specific data point.
• Be aware that these categories can overlap, such as a redundant copy of outdated data.

While there are three categories, they all have one thing in common: they’re not providing value to your business. And yet, you’re still paying storage costs and possibly leveraging outdated data in advanced AI use cases.

The Problem with ROT Data

Why is ROT data such an issue for organizations? While storing a few data points you don’t use anymore might not seem like an issue, at scale, it can create a significant issue with far-reaching effects. 

So, let’s break down why this type of data is a problem before exploring best practices to keep it under control.

Increases Data Storage and Maintenance Costs

Data needs to be stored, and even if never accessed, the hardware and software involved in data storage require maintenance. Generally, the more data stored, the higher these costs become.

The costs are typically worthwhile for useful data, but ROT data represents sunk costs in storing data that provides no value to your organization. Additionally, if you run regular backups, ROT data pushes these costs even higher.

Removing ROT data will reduce storage, backup, and maintenance costs. This reduction can be significant for organizations with a high volume of ROT data. Cutting costs is a powerful reason why enacting effective ROT data management practices is well worth the effort, which we’ll explore more below.

Reduced Productivity

Storing unnecessary data can make it more challenging for employees to find the data they need for their daily tasks. Requiring employees to sift through data to find what they need might not seem like a major slowdown, but even requiring a few extra minutes can add up over time and for large teams.

Simply searching for necessary data will take longer because the engine will need to parse through ROT data. Productivity can also be harmed if obsolete data is mistakenly considered relevant, creating issues that take time to correct.

More Data Sprawl Equals Expanded Attack Surface

Security and compliance is a major concern for any organization. In recent years, both initiatives have focused on data protection and implementing compliance-related processes. 

Storing ROT data can increase data security and compliance costs. Additionally, if not effectively protected, data that’s no longer useful to your business may still be useful to an attacker. Securing every byte of data is necessary, but securing ROT data is a sunk cost.

Regulatory requirements often stipulate when data should be deleted as well. Without the right data management practices, you may end up storing sensitive data for too long, risking your compliance standing in the process. It’s critical for organizations that face these types of requirements to have effective data management practices in place.

Negatively Affects AI Models

A recent and potent trend in the world of business data is training AI models for a variety of valuable use cases. From predictive analytics to autonomous customer service, business data is increasingly valuable.

However, obsolete data can skew the results of training an AI model, as the model will likely weigh ROT data as if it were relevant, useful data. As a result, the resulting model may be inaccurate or entirely flawed, which can introduce downstream issues once the model is put to work.

Developing and implementing effective ROT data management practices is mission-critical for any organization planning to explore emerging AI-based solutions. Training an AI model on irrelevant data will likely diminish its value to the organization. 

Key Best Practices to Manage ROT Data 

Implementing comprehensive ROT data management practices helps avoid the problems we explored above. You’ll cut costs, improve security and compliance, and get the most out of new AI-driven use cases.

How do you develop and enact processes to minimize ROT data? Let’s explore some best practices for a successful ROT data management program.

Remove Duplicate Data

Out of the three ROT categories, redundant data might be the easiest to identify and remove. Regularly review data across the entire data estate and identify precise duplicates. Leveraging automated tools can greatly simplify this task by handling the entire process or generating reports for technicians to act on.

Also known as data deduplication, this process is powered by understanding the primary data copy, the single source of truth (SSOT), and then identifying copies that should be removed.

A common area for implementing data deduplication is during data backups. Purpose-built solutions can use an SSOT to scan for any duplicates, skip them in the backup process, and possibly delete them depending on configurations.

Adopt Accurate Data Discovery and Classification Tools

Finding ROT data manually can be like finding a needle in a haystack. Additionally, handling these processes manually can be highly error-prone, as data that may seem obsolete might actually still be relevant.

Instead, implementing a data discovery and classification tool can automatically scan the entire data estate so you’re aware of every byte. From there, data can be classified into necessary categories, and finding redundant, obsolete, or trivial data can be more easily handled manually or by automated solutions.

On top of removing existing ROT data, you’ll also learn what processes or workflows are generating unnecessary and where they often live. For example, if data is duplicated for software staging areas but isn’t later deleted, you can make procedural changes to prevent ROT data at the source.

Develop and Enforce Data Retention Policies

ROT data is often the result of non-existent or ineffective data retention policies. Developing detailed policies for data retention and deletion can cut costs and minimize security risks.

Your specific policies will relate to your overall workflows, security needs, and compliance requirements. A data retention policy considers these varying requirements and crafts specific procedures that prevent ROT data from being created, define when and how it should be deleted, and the right platforms to back up new policies.

For industries like healthcare and financial services, compliance requirements often dictate how long sensitive and protected data must be stored. Policies won’t only be concerned with finding existing ROT data but also when relevant data becomes ROT data.

Partner with 1Touch to Keep ROT Data Under Control

ROT data can increase costs, introduce new security risks, and jeopardize compliance standing. If new AI use cases are explored, training models with ROT data mixed in with the training data can have far-reaching negative results.

Every organization must have specific data management practices that are concerned with identifying and securely destroying ROT data. The risks and expenses of leaving unnecessary data intact can be high but easily avoided.

Data discovery and classification is a crucial aspect of ROT data management. The right automated tool will continuously find ROT data to handle it, which typically means secure deletion appropriately.

Inventa by 1touch is an industry-leading data discovery and classification platform. Our reputable platform runs in the background, similar to an antivirus, and perpetually finds any ROT data throughout the IT ecosystem. Administrators can then act on these findings as necessary.

Is it time to get your ROT data under control? Book a demo today to see our platform in action and learn how Inventa can keep your operation cost-effective and secure.